Bid Value Determination

1. A method, comprising: generating, using a first loss function, a plurality of loss values associated with a plurality of sets of auction information, wherein a first loss value of the plurality of loss values is associated with a first set of auction information associated with a first auction; training a machine learning model using the first loss function, the plurality of loss values and the plurality of sets of auction information to generate a first machine learning model comprising a plurality of feature parameters associated with a plurality of features of the plurality of sets of auction information, wherein: the first loss function comprises a first value and a second value; the first value corresponds to: a first minimum bid value to win the first auction; or an optimal bid reduction factor determined based upon the first minimum bid value to win the first auction and a first bid value associated with a first content item; the second value corresponds to: a first shaded bid value associated with the first content item; or a bid reduction factor used to determine the first shaded bid value; the generating the plurality of loss values comprises generating the first loss value based upon a difference between the first value and the second value; and the first machine learning model is generated using a plurality of win-rates comprising a first win-rate corresponding to a first quantity of won auctions associated with the first shaded bid value; loading the first machine learning model onto a bid shading module of a demand-side platform (DSP), wherein the DSP is at least partially implemented by a DSP server; receiving, by the DSP at least partially implemented by the DSP server, a bid request from at least one of a supply-side platform (SSP) server or a content exchange server, wherein: the bid request is associated with a request for content associated with a client device; and the bid request is indicative of a set of features comprising one or more features associated with the request for content; determining a second bid value associated with a second content item; inputting, into the bid shading module of the DSP at least partially implemented by the DSP server, the second bid value and one or more first feature parameters, of the plurality of feature parameters, associated with the set of features; determining, using the first machine learning model loaded onto the bid shading module of the DSP at least partially implemented by the DSP server, a second shaded bid value associated with the second content item based upon the second bid value and the one or more first feature parameters, of the plurality of feature parameters, associated with the set of features; and submitting the second shaded bid value to an auction module that is at least partially implemented by at least one of the SSP server or the content exchange server for participation in a second auction associated with the request for content, wherein the second content item is provided for presentation on the client device associated with the request for content based upon a determination that the second content item is a winner of the second auction, wherein use of the first machine learning model that was generated using the plurality of win-rates increases a total win-rate associated with a plurality of auctions.

2. The method of claim 1, wherein the determining the second shaded bid value comprises: determining, based upon the one or more first feature parameters, the bid reduction factor; and applying the bid reduction factor to the second bid value to determine the second shaded bid value.

3. The method of claim 1, wherein: a first feature parameter, of the one or more first feature parameters, is associated with a first feature of the set of features; and the first feature parameter comprises: a first weight associated with the first feature; and a first vector representation of the first feature.

4. The method of claim 3, wherein the determining the second shaded bid value comprises: determining interactions between pairs of features of the set of features; and combining the interactions to determine the second shaded bid value.

5. The method of claim 4, wherein the determining the interactions comprises: determining a first interaction between the first feature and a second feature of the set of features based upon the first vector representation and a second vector representation, of a second feature parameter, associated with the second feature.

6. The method of claim 1, wherein the first win-rate is associated with one or more features corresponding to at least one of an internet resource, a domain name, a top-level domain or a web address.

7. The method of claim 1, wherein: the determining the first shaded bid value comprises: determining, based upon one or more feature parameters, the bid reduction factor; and applying the bid reduction factor to the first bid value to determine the first shaded bid value; and the second value corresponds to the bid reduction factor, the method comprising: determining the optimal bid reduction factor associated with the first auction based upon the first minimum bid value to win and the first bid value, wherein the first value to win corresponds to the optimal bid reduction factor.

8. The method of claim 7, wherein: the first loss value is greater if the optimal bid reduction factor exceeds the bid reduction factor by a first difference than if the optimal bid reduction factor is less than the bid reduction factor by the first difference.

9. The method of claim 1, wherein: the first value corresponds to the first minimum bid value; and the second value corresponds to the first shaded bid value.

10. The method of claim 9, wherein: the first loss value is greater if the first shaded bid value is less than the first minimum bid value by a first difference than if the first shaded bid value exceeds the first minimum bid value by the first difference.

11. The method of claim 1, wherein: the generating the first loss value is performed based upon a difference between the first bid value and the first minimum bid value.

12. The method of claim 1, wherein: the training the machine learning model to generate the first machine learning model is performed based upon the plurality of loss values.

13. The method of claim 1, wherein: the second auction is a first-price auction.

14. The method of claim 1, wherein: the first shaded bid value is less than the first bid value.

15. The method of claim 1, wherein: the second shaded bid value is less than the second bid value.

16. The method of claim 1, wherein: the set of features comprises at least one of: a second internet resource associated with the request for content; a second time of day associated with the request for content; a second day of week associated with the request for content; or a second location associated with the client device.

17. A computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising: generating, using a first loss function, a plurality of loss values associated with a plurality of sets of auction information, wherein a first loss value of the plurality of loss values is associated with a first set of auction information associated with a first auction; determining a first plurality of values associated with a plurality of auctions, wherein: the first plurality of values corresponds to: a plurality of minimum bid values to win indicated by the plurality of sets of auction information; or a plurality of optimal bid reduction factors determined based upon the plurality of minimum bid values to win and a plurality of bid values indicated by the plurality of sets of auction information; and a first value of the first plurality of values corresponds to: a first minimum bid value to win the first auction; or a first optimal bid reduction factor determined based upon the first minimum bid value to win the first auction and a first bid value associated with a first content item; determining a second plurality of values associated with the plurality of auctions, wherein: the second plurality of values corresponds to: a plurality of shaded bid values indicated by the plurality of sets of auction information; or a plurality of bid reduction factors used to determine the plurality of shaded bid values; a second value of the second plurality of values corresponds to: a first shaded bid value associated with the first content item; or a first bid reduction factor used to determine the first shaded bid value; and the generating the plurality of loss values comprises generating the first loss value based upon a difference between the first value and the second value; determining, based upon the first plurality of values and the second plurality of values, a plurality of differences, wherein a first difference of the plurality of differences corresponds to a difference between the first value and the second value; generating, based upon the plurality of sets of auction information and the plurality of differences, a plurality of feature parameters associated with a plurality of features of the plurality of sets of auction information; receiving, by a demand-side platform (DSP), a bid request, wherein: the bid request is associated with a second request for content associated with a client device; and the bid request is indicative of a set of features comprising one or more features associated with the second request for content; determining a second bid value associated with a second content item; identifying one or more first feature parameters, of the plurality of feature parameters, associated with the set of features; inputting, into a bid shading module of the DSP, the second bid value and the one or more first feature parameters, wherein the bid shading module of the DSP uses a plurality of win-rates comprising a first win-rate corresponding to a first quantity of won auctions associated with the first shaded bid value; determining, using the bid shading module of the DSP and based upon the one or more first feature parameters and the second bid value, a second shaded bid value; and submitting the second shaded bid value to an auction module for participation in a second auction associated with the second request for content, wherein the second content item is provided for presentation on the client device associated with the second request for content based upon a determination that the second content item is a winner of the second auction, wherein use of the plurality of win-rates by the bid shading module of the DSP increases a total win-rate associated with a plurality of auctions.

18. The computing device of claim 17, wherein: the first plurality of values corresponds to the plurality of minimum bid values; and the second plurality of values corresponds to the plurality of shaded bid values.

19. The computing device of claim 17, wherein: the determining the first shaded bid value comprises: determining, based upon one or more feature parameters, the first bid reduction factor; and applying the first bid reduction factor to the first bid value to determine the first shaded bid value; and the second plurality of values corresponds to the plurality of bid reduction factors, comprising the first bid reduction factor, associated with the plurality of auctions, the operations comprising: determining the first optimal bid reduction factor associated with the first auction based upon the first minimum bid value to win and the first bid value, wherein the first plurality of values corresponds to the plurality of optimal bid reduction factors, comprising the first optimal bid reduction factor, associated with the plurality of auctions.

20. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising: generating, using a first loss function, a plurality of loss values associated with a plurality of sets of auction information, wherein a first loss value of the plurality of loss values is associated with a first set of auction information associated with a first auction; training a machine learning model using the first loss function, the plurality of loss values and the plurality of sets of auction information to generate a first machine learning model comprising a plurality of feature parameters associated with a plurality of features of the plurality of sets of auction information, wherein: the first loss function comprises a first value and a second value; the first value corresponds to: a first minimum bid value to win the first auction; or an optimal bid reduction factor determined based upon the first minimum bid value to win the first auction and a first bid value associated with a first content item; the second value corresponds to: a first shaded bid value associated with the first content item; or a bid reduction factor used to determine the first shaded bid value; the generating the plurality of loss values comprises generating the first loss value based upon a difference between the first value and the second value; and the first machine learning model is generated using a plurality of win-rates comprising a first win-rate corresponding to a first quantity of won auctions associated with the first shaded bid value; loading the first machine learning model onto a bid shading module of a demand-side platform (DSP), wherein the DSP is at least partially implemented by a DSP server; receiving, by the DSP at least partially implemented by the DSP server, a bid request from at least one of a supply-side platform (SSP) server or a content exchange server, wherein: the bid request is associated with a request for content associated with a client device; and the bid request is indicative of a set of features comprising one or more features associated with the request for content; determining a second bid value associated with a second content item; inputting, into the bid shading module of the DSP at least partially implemented by the DSP server, the second bid value and one or more first feature parameters, of the plurality of feature parameters, associated with the set of features; determining, using the first machine learning model loaded onto the bid shading module of the DSP at least partially implemented by the DSP server, a second shaded bid value associated with the second content item based upon the second bid value and the one or more first feature parameters, of the plurality of feature parameters, associated with the set of features; and submitting the second shaded bid value to an auction module that is at least partially implemented by at least one of the SSP server or the content exchange server for participation in a second auction associated with the request for content, wherein the second content item is provided for presentation on the client device associated with the request for content based upon a determination that the second content item is a winner of the second auction, wherein use of the first machine learning model that was generated using the plurality of win-rates increases a total win-rate associated with a plurality of auctions.